Senior Customer Success Architect (AI SDLC)

What you'll do

We’re hiring a Customer Success Architect (AI-SDLC) to help enterprise customers put AI to work across the software development lifecycle, from planning and knowledge discovery to development, review, testing, and release. This individual contributor role sits within our Customer Success Architect team and reports to a regional leader.

In this role, you’ll lead time-boxed technical engagements and build solutions alongside customers, connecting Atlassian workflows with enterprise knowledge and the development tools their teams use. The work combines technical depth, customer-facing consulting, and software delivery expertise. Working with engineering and platform teams, you’ll identify high-impact opportunities, validate working solutions, and establish measurable improvements in adoption, developer experience, and software delivery.

You’ll also partner with Account Teams, Customer Success, and Product teams to bring customers’ planning, knowledge, and development workflows together. You’ll help engineering teams move beyond isolated AI experiments and turn their investments into practical, repeatable ways of working, with connected workflows and clear governance. As customers achieve results, you’ll help them share what worked and what they learned with their peers.

As a senior individual contributor, you’ll contribute customer insight, practical patterns, and field feedback that help Atlassian improve how its software delivery products are adopted across enterprise customers. You will also serve as a mentor for your teammates, sharing your learnings leveraging your experience to upskill the entire team.

Responsibilities

On your first day, we'll expect you to have:

• Discovery: Assess how customers deliver software, the development tools they use, and their readiness to adopt AI. Partner with engineering, platform, and developer experience teams to find bottlenecks, gaps in context, and repetitive work across planning, development, review, testing, and release. Help customers prioritize AI use cases with clear adoption goals and measurable delivery outcomes.

• Design: Work with customers to design AI-enabled workflows that connect requirements, team knowledge, and development tools. Help shape practical use cases such as development readiness, knowledge discovery, implementation planning, and review preparation. For each workflow, define the integrations and context sources it requires, where human review fits, and how to assess its success.

• Deploy: Guide customers through time-boxed pilots and technical activation, working alongside their teams to validate workflows in their environment. Measure adoption, output quality, and delivery impact against agreed baselines. Give customers the guidance and ongoing feedback processes they need to maintain, refine, and scale successful practices independently.

• Collaborate: Build coordinated technical activation plans with Account Teams, CSMs, and relevant product specialists. Partner with customer engineering, platform, security, and AI stakeholders to bring AI-enabled practices into their existing toolchains and ways of working. Help address the adoption barriers and technical dependencies involved.

• Advocate: Share insights from customer feedback, adoption patterns, and technical friction that relevant Atlassian product and engineering teams can act on. Document validated use cases, reusable patterns, and customer outcomes to improve future engagements and help scale AI-SDLC adoption.

• Travel up to 15% domestically and internationally for customer and internal meetings.

Qualifications

Your background

• Customer success and advisory experience. 7–10+ years in customer success, solutions architecture, consulting, transformation, or technical advisory roles supporting enterprise customers with complex SaaS environments. Proven experience leading strategic post-sales engagements without direct managerial authority.

• Technical Aptitude: Hands-on experience configuring and integrating tools used by software development teams, including Jira, Confluence, and Git-based development platforms or comparable tools such as Linear. Comfortable reading code, working with APIs, troubleshooting integrations, and building lightweight automation or prototypes. Practical experience with AI-assisted development tools or agents, including preparing relevant context and evaluating outputs for accuracy and usefulness.

• Solution Area Expertise: Strong understanding of the software development lifecycle, including planning, version control, code review, testing, continuous integration and delivery, and release practices. Hands-on familiarity with AI-assisted development tools and how to incorporate them into team workflows with appropriate context, human oversight, and quality controls.

• Product Mindset & Communication: Strong product mindset with the ability to translate technical AI capabilities into clear outcomes for engineering leaders and executive stakeholders, such as shorter delivery cycles, reduced coordination effort, improved developer experience, and maintained or improved quality. Able to communicate technical trade-offs and balance rapid experimentation with security, governance, and reliability.

• Change leadership and adoption design: Track record of helping customers adopt new ways of working, not just new tools. Experience designing phased rollouts, stakeholder engagement plans, enablement strategies, and governance approaches that drive sustained usage.

• Stakeholder communication: Strong communication skills with the ability to translate technical and product capabilities into business outcomes. Comfortable facilitating conversations with senior technology leaders, digital workplace owners, engineering leaders, transformation teams, and platform administrators.

• Programmatic mindset: Experience contributing to reusable plays, enablement content, customer guidance, or adoption methodologies that can scale across a team or region.

• AI and modern work fluency: Familiarity with AI-powered work, enterprise knowledge workflows, and how capabilities such as Rovo can enhance collaboration, search, decision support, and execution across teams.